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Learning and Recognizing the Places We Go

  • Jeffrey Hightower
  • Sunny Consolvo
  • Anthony LaMarca
  • Ian Smith
  • Jeff Hughes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3660)

Abstract

Location-enhanced mobile devices are becoming common, but applications built for these devices find themselves suffering a mismatch between the latitude and longitude that location sensors provide and the colloquial place label that applications need. Conveying my location to my spouse, for example as (48.13641N, 11.57471E), is less informative than saying “at home.” We introduce an algorithm called BeaconPrint that uses WiFi and GSM radio fingerprints collected by someone’s personal mobile device to automatically learn the places they go and then detect when they return to those places. BeaconPrint does not automatically assign names or semantics to places. Rather, it provides the technological foundation to support this task. We compare BeaconPrint to three existing algorithms using month-long trace logs from each of three people. Algorithmic results are supplemented with a survey study about the places people go. BeaconPrint is over 90% accurate in learning and recognizing places. Additionally, it improves accuracy in recognizing places visited infrequently or for short durations—a category where previous approaches have fared poorly. BeaconPrint demonstrates 63% accuracy for places someone returns to only once or visits for less than 10 minutes, increasing to 80% accuracy for places visited twice.

Keywords

Global Position System Mobile Device Data Collector Ubiquitous Computing Visit Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jeffrey Hightower
    • 1
  • Sunny Consolvo
    • 1
  • Anthony LaMarca
    • 1
  • Ian Smith
    • 1
  • Jeff Hughes
    • 2
  1. 1.Intel Research SeattleSeattleUSA
  2. 2.Computer Science & EngineeringUniversity of WashingtonSeattleUSA

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